Book Image

Learning Geospatial Analysis with Python

By : Joel Lawhead
4 (1)
Book Image

Learning Geospatial Analysis with Python

4 (1)
By: Joel Lawhead

Overview of this book

Geospatial analysis is used in almost every field you can think of from medicine, to defense, to farming. It is an approach to use statistical analysis and other informational engineering to data which has a geographical or geospatial aspect. And this typically involves applications capable of geospatial display and processing to get a compiled and useful data. "Learning Geospatial Analysis with Python" uses the expressive and powerful Python programming language to guide you through geographic information systems, remote sensing, topography, and more. It explains how to use a framework in order to approach Geospatial analysis effectively, but on your own terms. "Learning Geospatial Analysis with Python" starts with a background of the field, a survey of the techniques and technology used, and then splits the field into its component speciality areas: GIS, remote sensing, elevation data, advanced modelling, and real-time data. This book will teach you everything there is to know, from using a particular software package or API to using generic algorithms that can be applied to Geospatial analysis. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don't become bogged down in just getting ready to do analysis. "Learning Geospatial Analysis with Python" will round out your technical library with handy recipes and a good understanding of a field that supplements many a modern day human endeavors.
Table of Contents (17 chapters)
Learning Geospatial Analysis with Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Metadata management


Internet distribution of data has increased the importance of metadata. Data custodians are able to release a data set to the entire world for download without any personal interaction. The metadata record of a geospatial data set can follow it to help ensure the integrity and accountability for that data is maintained. Properly formatted metadata also allows for automated cataloguing, search indexing, and integration of data sets. Metadata has become so important that a common mantra within the geospatial community is "Data without metadata isn't data", meaning that a geospatial data set cannot be fully utilized and understood without metadata. The following section will list some of the common metadata tools which are available.

GeoNetwork

GeoNetwork is an open source, Java-based catalog server to manage geospatial data. It includes a metadata editor and search engine, as well as an interactive web map viewer. The system is designed to connect spatial data infrastructures...